建筑房屋动力特性的测试与分析

以大地脉动作为激励振源，对房屋的脉动信号及动力特性进行了测试和分析，指出了通过特殊的仪器，在没有大型激振的情况下，通过分析大地的脉动对结构的影响，可以对房屋进行动力特性的分析。     

国外城市社会空间演变的动力机制研究综述及政策启示

城市社会空间演变的动力机制问题一直是国外城市社会学、地理学、人口学、规划学等学科研究的热点，形成了各种理论流派。当前，在经济全球化和市场化改革不断深入的背景下，中国城市社会空间正发生类似于西方城市社会空间的演变趋势。因此，对国外城市社会空间演变的动力机制理论进行研究，可以为正在发生的中国城市社会空间问题提供可供借鉴的理论解释，并且也为合理的城市治理公共政策提供理论依据。     

状态变量迭代法分析结构动力响应问题

在分析了状态变量法主要特点的基础上 ,建立了结构动力响应分析的状态变量方程 ,使用状态变量迭代计算格式来计算结构的动力响应量。文中给出了数值算例 ,由数值结果表明 ,状态变量法具有计算量少 ,效率高和精度好的一些特点。     

Evaluating sociotechnical dynamics in a simulated remotely-piloted aircraft system: a layered dynamics approach

As coordination mechanisms change and technology failures occur, a sociotechnical system must reorganise itself across human and technological layers to maintain effectiveness. We present a study examining reorganisation across communication, controls and vehicle layers of a remotely-piloted aircraft system (RPAS) using a layered dynamics approach. Team members (pilot; navigator; photographer) performed 5 simulated RPAS missions using different operator configurations, including all-human and human-autonomy teams. Reorganization (operationally defined using entropy) time series measured the changing system reorganisation profiles under different operator configurations and following autonomy failures. Correlations between these reorganisation profiles and team effectiveness scores describe the manner in which the system had to be coordinated to maintain effectiveness under these changing conditions. Four unplanned autonomy failures were analysed to visualise system reorganisation following a technology failure. With its objective and real-time modelling and measurement capabilities, layered dynamics complements existing systems thinking tools for understanding sociotechnical complexity and enhancing system effectiveness.

Practitioner summary: A layered dynamics approach for understanding how a sociotechnical system dynamically reorganises itself is presented. The layered dynamics of RPAS were analysed under different operator configurations and following autonomy failures. Layered dynamics complements existing system-thinking tools for modelling sociotechnical system complexity and effectiveness.

Abbreviation: RPAS: remotely-piloted aircraft system; HIS: human-systems integration; EAST: event analysis of systemic teamwork; H1: hypothesis 1; H2: hypothesis 2; H3: hypothesis 3; CERTT-STE: cognitive engineering research on team tasks--synthetic task environment; AVO: air vehicle operator; PLO: payload operator; DEMPC: data exploitation, mission planning, and communications; ACT-R: adaptive control of thought-rational; sec: seconds; ANOVA: analysis of variance     

A general framework for analyzing techno-behavioural dynamics on networks

A general framework for assessing future impacts of technology on society and environment is presented. The dynamics between human activity and technological systems impact upon many processes in society and nature. This involves non-linear dynamics requiring an understanding of how technology and human behaviour influence each other and co-evolve. Conventionally, technological and behavioural systems are analyzed as separate entities. We develop an integrated theoretical and methodological approach termed techno-behavioural dynamics focussing on networked interactions between technology and behaviour across multiple system states. We find that positive feedback between technology learning, evolving preferences and network effects can lead to tipping points in complex sociotechnical systems. We also demonstrate how mean-field and agent-based models are complimentary for capturing a hierarchy of analytical resolutions in a common problem domain. Assessing and predicting co-evolutionary dynamics between technology and human behaviour can help avoid systems lock-in and inform a range of adaptive responses to environmental and societal risk.     

Regressor selection for ozone prediction

Being able to predict high concentrations of tropospheric ozone is important because of its negative impact on human health. In this paper eight regressor-selection methods are utilised in a case study for ozone prediction in the city of Nova Gorica, Slovenia. The comparison of the selected methods proved to be useful for building models that successfully predict the ozone concentrations for the treated case. Different regressors are selected for different models, with different methods based on the validation procedure’s cost functions. Namely, for the model to predict the maximum daily ozone concentration, ten regressors are selected; for the average concentration of ozone between 8.00 and 20.00 h, fifteen regressors are selected; and for the average daily concentration, ten regressors are selected. The result of the study is a regressor selection that is specific for a particular geographical location. Moreover, the study reveals that regressor selection, as well as the obtained models, differ depending on the kind of averaging interval of the ozone concentration.     

Robustness of declarative modeling languages: Improvements via probability-one homotopy

Robustness issues with steady-state initialization remain a barrier in the practical use of declarative modeling languages for multi-domain modeling of large, complex, and heterogeneous technical systems. The objective of this paper is to illustrate how probability-one homotopy, an established method from topology, can solve this issue. This is achieved by establishing a framework for application-specific probability-one homotopy in declarative modeling languages. The analysis is based on domain-specific probability-one homotopy maps, which were reformulated in a declarative fashion. Additionally, a novel probability-one homotopy map and associated coercivity proof is introduced for a class of thermo-fluid dynamics problems. It was found that the approach enables robust initialization for declarative modeling languages on several test cases and leads to a concise declarative problem formulation.     

Prediction of Particlulate Air Pollution using Neural Techniques

We have analysed the possibility of predicting hourly average concentrations of suspended atmospheric particulate matter with aerodynamic diameter less than 2.5 microns (PM2.5) several hours in advance using data obtained in downtown Santiago, Chile. By performing some standard tests used in the study of dynamical systems, we are able to extract some features of the time series of data. We use this information to estimate the amount of data on the past to be used as input to a neural network in order to predict future values of PM2.5 concentrations. We show that improvement of predictions is possible by using another neural network for noise reduction on the original series. The best results are obtained with a type of neural network which is equivalent to a linear regression. Up to six hours in advance, predictions generated in this way have significantly smaller errors than predictions based on the persistence of the long term average of the data.     

Dynamic pattern denoising method using multi-basin system with kernels

In this paper, we propose a novel pattern denoising method that utilizes the topological property of a support that describes the distribution of normal patterns to denoise noisy patterns. The method first trains a support function which captures the domain of normal patterns and then construct a so-called multi-basin system associated with the trained support function. By moving noisy patterns along the trajectories of the multi-basin system, noise is removed while the pattern recovers its normality. The denoised pattern is obtained when the noisy pattern arrives at the attracting manifold generated by a set of normal patterns and this is the most similar normal pattern with the noisy pattern in the topological sense. Through simulations on some toy dataset and real image datasets, we show that the proposed framework effectively removes the noise while preserving the information contained in the noisy pattern.     

An approximate inference with Gaussian process to latent functions from uncertain data

Most formulations of supervised learning are often based on the assumption that only the outputs data are uncertain. However, this assumption might be too strong for some learning tasks. This paper investigates the use of Gaussian processes to infer latent functions from a set of uncertain input-output examples. By assuming Gaussian distributions with known variances over the inputs-outputs and using the expectation of the covariance function, it is possible to analytically compute the expected covariance matrix of the data to obtain a posterior distribution over functions. The method is evaluated on a synthetic problem and on a more realistic one, which consist in learning the dynamics of a cart-pole balancing task. The results indicate an improvement of the mean squared error and the likelihood of the posterior Gaussian process when the data uncertainty is significant.